Samrad Jafarian-Namin, Alireza Goli, Mojtaba Qolipour, Ali Mostafaeipour and Amir-Mohammad Golmohammadi
The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.
Abstract
Purpose
The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.
Design/methodology/approach
The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.
Findings
The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.
Originality/value
Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.
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Ali Mostafaeipour, Mojtaba Qolipour, Mostafa Rezaei, Mehdi Jahangiri, Alireza Goli and Ahmad Sedaghat
Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting…
Abstract
Purpose
Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives.
Design/methodology/approach
In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software.
Findings
Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified.
Originality/value
As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.
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Misagh Rahbari, Alireza Arshadi Khamseh and Yaser Sadati-Keneti
The Russia–Ukraine war has disrupted the wheat supply worldwide. Given that wheat is one of the most important agri-food products in the world, it is necessary to pay attention to…
Abstract
Purpose
The Russia–Ukraine war has disrupted the wheat supply worldwide. Given that wheat is one of the most important agri-food products in the world, it is necessary to pay attention to the wheat supply chain during the global crises. The use of resilience strategies is one of the solutions to face the supply chain disruptions. In addition, there is a possibility of multiple crises occurring in global societies simultaneously.
Design/methodology/approach
In this research, the resilience strategies of backup suppliers (BS) and inventory pre-prepositioning (IP) were discussed in order to cope with the wheat supply chain disruptions. Furthermore, the p-Robust Scenario-based Stochastic Programming (PRSSP) approach was used to optimize the wheat supply chain under conditions of disruptions from two perspectives, feasibility and optimality.
Findings
After implementing the problem of a real case in Iran, the results showed that the use of resilience strategy reduced costs by 9.33%. It was also found that if resilience strategies were used, system's flexibility and decision-making power increased. Besides, the results indicated that if resilience strategies were used and another crisis like the COVID-19 pandemic occurred, supply chain costs would increase less than when resilience strategies were not used.
Originality/value
In this study, the design of the wheat supply chain was discussed according to the wheat supply disruptions due to the Russia–Ukraine war and its implementation on a real case. In the following, various resilience strategies were used to cope with the wheat supply chain disruptions. Finally, the effect of the COVID-19 pandemic on the wheat supply chain in the conditions of disruptions caused by the Russia–Ukraine war was investigated.
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Alireza Bakhshi, Amir Aghsami and Masoud Rabbani
Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply…
Abstract
Purpose
Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud.
Design/methodology/approach
This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency.
Findings
This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities.
Originality/value
Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case study.
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Yasser M. Ibrahim and Rasha Hassan
Blockchain (BC) has recently gained attention from educational organizations as a reliable and trustworthy technology for storing crucial data. This study aims to explore the…
Abstract
Purpose
Blockchain (BC) has recently gained attention from educational organizations as a reliable and trustworthy technology for storing crucial data. This study aims to explore the factors that influence the students’ intention to use BC to host an automated reward system that is based on objective criteria of students’ performance and activities. Predefined smart contracts would guarantee unbiased judgements and fair rewards.
Design/methodology/approach
A mixed method approach is used. Based on an iterative rounds of Delphi approach along with a comprehensive literature review, this research proposed an extended Unified Theory of Acceptance and Use of Technology model. The model hypotheses relate performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), perceived innovativeness (PI), trust (TR), perceived security and privacy (S&P), complexity (CX) and enjoyment (EN) to students behavioural intention to use the reward system. The study tests the hypotheses using survey data from 138 students who are familiar with BC technology and its applications. The data analysis is performed using partial least squares-structural equation modelling method.
Findings
The analysis revealed that PE, EE, PI, TR and EN positively affect students’ intention to use the BC rewarding system, while CX negatively impacts it. Conversely, FC and, interestingly, S&P, which showed a significant influence on trust, were found to be insignificant in influencing students’ intention to use the system.
Originality/value
This study significantly contributes to the educational technology field by addressing research gaps regarding the adoption and acceptance of BC technology in academia. Firstly, it proposes a basic form of a student reward system. Secondly, it introduces an empirically validated model of students’ adoption, pinpointing factors influencing their readiness to embrace a BC reward system and providing practical implementation insights.